• arXiv.cs.MS Pub Date : 2020-09-16
Boro Sofranac; Ambros Gleixner; Sebastian Pokutta

Fast domain propagation of linear constraints has become a crucial component of today's best algorithms and solvers for mixed integer programming and pseudo-boolean optimization to achieve peak solving performance. Irregularities in the form of dynamic algorithmic behaviour, dependency structures, and sparsity patterns in the input data make efficient implementations of domain propagation on GPUs and

更新日期：2020-09-18
• arXiv.cs.MS Pub Date : 2020-09-16
Luca Parisi

This paper describes the 'm-arcsinh', a modified ('m-') version of the inverse hyperbolic sine function ('arcsinh'). Kernel and activation functions enable Machine Learning (ML)-based algorithms, such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), to learn from data in a supervised manner. m-arcsinh, implemented in the open source Python library 'scikit-learn', is hereby presented

更新日期：2020-09-18
• arXiv.cs.MS Pub Date : 2020-09-10
Simon Praetorius; Florian Stenger

In this paper we introduce and describe an implementation of curved surface geometries within the Dune framework for grid-based discretizations. Therefore, we employ the abstraction of geometries as local-functions bound to a grid element, and the abstraction of a grid as connectivity of elements together with a grid-function that can be localized to the elements to provide element local parametrizations

更新日期：2020-09-11
• arXiv.cs.MS Pub Date : 2020-09-04
Gergely Máté Kiss; Jan Kaska; Roberto André Henrique de Oliveira; Olena Rubanenko; Balázs Tóth

The paper presents a comparative analysis of different commercial and academic software. The comparison aims to examine how the integrated adaptive grid refinement methodologies can deal with challenging, electromagnetic-field related problems. For this comparison, two benchmark problems were examined in the paper. The first example is a solution of an L-shape domain like test problem, which has a

更新日期：2020-09-10
• arXiv.cs.MS Pub Date : 2020-09-07
Raphael Sonabend; Franz Kiraly

distr6 is an object-oriented (OO) probability distributions interface leveraging the extensibility and scalability of R6, and the speed and efficiency of Rcpp. Over 50 probability distributions are currently implemented in the package with core' methods including density, distribution, and generating functions, and more exotic' ones including hazards and distribution function anti-derivatives. In

更新日期：2020-09-08
• arXiv.cs.MS Pub Date : 2020-08-29
N. Montana Brown; Y. Fu; S. U. Saeed; A. Casamitjana; Z. M. C. Baum; R. Delaunay; Q. Yang; A. Grimwood; Z. Min; E. Bonmati; T. Vercauteren; M. J. Clarkson; Y. Hu

This document outlines a tutorial to get started with medical image registration using the open-source package DeepReg. The basic concepts of medical image registration are discussed, linking classical methods to newer methods using deep learning. Two iterative, classical algorithms using optimisation and one learning-based algorithm using deep learning are coded step-by-step using DeepReg utilities

更新日期：2020-09-08
• arXiv.cs.MS Pub Date : 2020-09-02
Drew Schmidt

The Singular Value Decomposition (SVD) is one of the most important matrix factorizations, enjoying a wide variety of applications across numerous application domains. In statistics and data analysis, the common applications of SVD such as Principal Components Analysis (PCA) and linear regression. Usually these applications arise on data that has far more rows than columns, so-called "tall/skinny"

更新日期：2020-09-03
• arXiv.cs.MS Pub Date : 2020-08-28
Alexis Montoison; Dominique Orban

We introduce iterative methods named TriCG and TriMR for solving symmetric quasi-definite systems based on the orthogonal tridiagonalization process proposed by Saunders, Simon and Yip in 1988. TriCG and TriMR are tantamount to preconditioned block-CG and block-MINRES with two right-hand sides in which the two approximate solutions are summed at each iteration, but require less storage and work per

更新日期：2020-09-01
• arXiv.cs.MS Pub Date : 2020-08-26
Daniela Vorkel; Robert Haase

This chapter introduces GPU-accelerated image processing in ImageJ/FIJI. The reader is expected to have some pre-existing knowledge of ImageJ Macro programming. Core concepts such as variables, for-loops, and functions are essential. The chapter provides basic guidelines for improved performance in typical image processing workflows. We present in a step-by-step tutorial how to translate a pre-existing

更新日期：2020-08-28
• arXiv.cs.MS Pub Date : 2020-08-22
Leonid B. Sokolinsky

The BSF-skeleton is designed for creating parallel programs in C++ using the MPI library. The scope of the BSF-skeleton is cluster computing systems and iterative numerical algorithms of high computational complexity. The BSF-skeleton completely encapsulates all aspects that are associated with parallelizing a program on a cluster computing system. The source code of the BSF-skeleton is freely available

更新日期：2020-08-28
• arXiv.cs.MS Pub Date : 2020-08-21
Ryan Bernstein; Matthijs Vákár; Jeannette Wing

Probabilistic programming is perfectly suited to reliable and transparent data science, as it allows the user to specify their models in a high-level language without worrying about the complexities of how to fit the models. Static analysis of probabilistic programs presents even further opportunities for enabling a high-level style of programming, by automating time-consuming and error-prone tasks

更新日期：2020-08-25
• arXiv.cs.MS Pub Date : 2020-08-19
Yuhsiang Mike Tsai; Terry Cojean; Hartwig Anzt

GPU accelerators have become an important backbone for scientific high performance computing, and the performance advances obtained from adopting new GPU hardware are significant. In this paper we take a first look at NVIDIA's newest server line GPU, the A100 architecture part of the Ampere generation. Specifically, we assess its performance for sparse linear algebra operations that form the backbone

更新日期：2020-08-20
• arXiv.cs.MS Pub Date : 2020-08-16
Anjali Sandip

Open-source electromagnetic design software, Elmer FEM, was interfaced with data analytics toolkit, Dakota. Furthermore, the coupled software was validated against a benchmark test. The interface developed provides a unified open-source computational framework for electromagnetics and data analytics. Its key features include uncertainty quantification, surrogate modelling and parameter studies. This

更新日期：2020-08-18
• arXiv.cs.MS Pub Date : 2020-08-12
Jens Hahne; Stephanie Friedhoff; Matthias Bolten

In this paper, we introduce the Python framework PyMGRIT, which implements the multigrid-reduction-in-time (MGRIT) algorithm for solving the (non-)linear systems arising from the discretization of time-dependent problems. The MGRIT algorithm is a reduction-based iterative method that allows parallel-in-time simulations, i. e., calculating multiple time steps simultaneously in a simulation, by using

更新日期：2020-08-14
• arXiv.cs.MS Pub Date : 2020-08-10
Jed A. Duersch; Ming Gu

Rank-revealing matrix decompositions provide an essential tool in spectral analysis of matrices, including the Singular Value Decomposition (SVD) and related low-rank approximation techniques. QR with Column Pivoting (QRCP) is usually suitable for these purposes, but it can be much slower than the unpivoted QR algorithm. For large matrices, the difference in performance is due to increased communication

更新日期：2020-08-12
• arXiv.cs.MS Pub Date : 2020-08-10
Jonas Rauber; Matthias Bethge; Wieland Brendel

EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch, TensorFlow, JAX, and NumPy. Library developers no longer need to choose between supporting just one of these frameworks or reimplementing the library for each framework and dealing with code duplication. Users of such libraries can more easily switch frameworks without being locked in by a specific

更新日期：2020-08-11
• arXiv.cs.MS Pub Date : 2020-08-05
Xia Liao; Shengguo Li; Yutong Lu; Jose E. Roman

In this paper, a parallel structured divide-and-conquer (PSDC) eigensolver is proposed for symmetric tridiagonal matrices based on ScaLAPACK and a parallel structured matrix multiplication algorithm, called PSMMA. Computing the eigenvectors via matrix-matrix multiplications is the most computationally expensive part of the divide-and-conquer algorithm, and one of the matrices involved in such multiplications

更新日期：2020-08-06
• arXiv.cs.MS Pub Date : 2020-07-31
Garrett Wright

Edit Distance is a classic family of dynamic programming problems, among which Time Warp Edit Distance refines the problem with the notion of a metric and temporal elasticity. A novel Improved Time Warp Edit Distance algorithm that is both massively parallelizable and requiring only linear storage is presented. This method uses the procession of a three diagonal band to cover the original dynamic program

更新日期：2020-08-03
• arXiv.cs.MS Pub Date : 2020-07-29
Robert M. Gower; Margarida P. Mello

We investigate the computation of Hessian matrices via Automatic Differentiation, using a graph model and an algebraic model. The graph model reveals the inherent symmetries involved in calculating the Hessian. The algebraic model, based on Griewank and Walther's state transformations, synthesizes the calculation of the Hessian as a formula. These dual points of view, graphical and algebraic, lead

更新日期：2020-07-31
• arXiv.cs.MS Pub Date : 2020-07-28
Matthew Fishman; Steven R. White; E. Miles Stoudenmire

ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagram notation, which allows users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interface rules out common programming errors and enables rapid prototyping of tensor network algorithms. After discussing the philosophy behind the ITensor approach

更新日期：2020-07-30
• arXiv.cs.MS Pub Date : 2020-07-26
Jannik MichelfeitTechnische Universität Dresden

Many applications in the sciences require numerically stable and computationally efficient evaluation of multivariate polynomials. Finding beneficial representations of polynomials, such as Horner factorisations, is therefore crucial. multivar_horner, the python package presented here, is the first open source software for computing multivariate Horner factorisations. This work briefly outlines the

更新日期：2020-07-28
• arXiv.cs.MS Pub Date : 2020-07-26
Zijing Gu

We implemented and optimized matrix multiplications between dense and block-sparse matrices on CUDA. We leveraged TVM, a deep learning compiler, to explore the schedule space of the operation and generate efficient CUDA code. With the automatic parameter tuning in TVM, our cross-thread reduction based implementation achieved competitive or better performance compared with other state-of-the-art frameworks

更新日期：2020-07-28
• arXiv.cs.MS Pub Date : 2020-07-22

Computational implementations for solving systems of linear equations often rely on a one-size-fits-all approach based on LU decomposition of dense matrices stored in column-major format. Such solvers are typically implemented with the aid of the xGESV set of functions available in the low-level LAPACK software, with the aim of reducing development time by taking advantage of well-tested routines.

更新日期：2020-07-23
• arXiv.cs.MS Pub Date : 2020-07-21
Rüdiger Zeller; Olaf Delgado Friedrichs; Daniel H. Huson

Periodic tilings play a role in the decorative arts, in construction and in crystal structures. Combinatorial tiling theory allows the systematic generation, visualization and exploration of such tilings of the plane, sphere and hyperbolic plane, using advanced algorithms and software.Here we present a "galaxy" of tilings that consists of the set of all 2.4 billion different types of periodic tilings

更新日期：2020-07-22
• arXiv.cs.MS Pub Date : 2020-07-19
Migran N. Gevorkyan; Anna V. Korolkova; Dmitry S. Kulyabov

In problems of mathematical physics, to study the structures of spaces using the Cayley-Klein models in theoretical calculations, the use of generalized complex numbers is required. In the case of computational experiments, such tasks require their high-quality implementation in a programming language. The proposed small implementation of generalized complex numbers in modern programming languages

更新日期：2020-07-21
• arXiv.cs.MS Pub Date : 2020-07-18
Anna Maria Yu. Apreutesey; Anna V. Korolkova; Dmitry S. Kulyabov

This work is devoted to the study of the capabilities of the Modelica and Julia programming languages for the implementation of a continuously discrete paradigm in modeling hybrid systems that contain both continuous and discrete aspects of behavior. A system consisting of an incoming stream that is processed according to the Transmission Control Protocol (TCP) and a router that processes traffic using

更新日期：2020-07-21
• arXiv.cs.MS Pub Date : 2020-07-15
Kyaw L. Oo; Andreas Vogel

With the hardware support for half-precision arithmetic on NVIDIA V100 GPUs, high-performance computing applications can benefit from lower precision at appropriate spots to speed up the overall execution time. In this paper, we investigate a mixed-precision geometric multigrid method to solve large sparse systems of equations stemming from discretization of elliptic PDEs. While the final solution

更新日期：2020-07-16
• arXiv.cs.MS Pub Date : 2020-07-13
Flavien Quijoux; Charles Truong; Aliénor Vienne-Jumeau; Laurent Oudre; François BERTIN-HUGAULT; Philippe ZAWIEJA; Marie LEFEVRE; Pierre-Paul VIDAL; Damien RICARD

Meta-analysis is a data aggregation method that establishes an overall and objective level of evidence based on the results of several studies. It is necessary to maintain a high level of homogeneity in the aggregation of data collected from a systematic literature review. However, the current tools do not allow a cross-referencing of the experimental conditions that could explain the heterogeneity

更新日期：2020-07-16
• arXiv.cs.MS Pub Date : 2020-07-13
Ahmad Abdelfattah; Hartwig Anzt; Erik G. Boman; Erin Carson; Terry Cojean; Jack Dongarra; Mark Gates; Thomas Grützmacher; Nicholas J. Higham; Sherry Li; Neil Lindquist; Yang Liu; Jennifer Loe; Piotr Luszczek; Pratik Nayak; Sri Pranesh; Siva Rajamanickam; Tobias Ribizel; Barry Smith; Kasia Swirydowicz; Stephen Thomas; Stanimire Tomov; Yaohung M. Tsai; Ichitaro Yamazaki; Urike Meier Yang

Within the past years, hardware vendors have started designing low precision special function units in response to the demand of the Machine Learning community and their demand for high compute power in low precision formats. Also the server-line products are increasingly featuring low-precision special function units, such as the NVIDIA tensor cores in ORNL's Summit supercomputer providing more than

更新日期：2020-07-15
• arXiv.cs.MS Pub Date : 2020-07-12
Lambert Theisen; Manuel Torrilhon

We present a mixed finite element solver for the linearized R13 equations of non-equilibrium gas dynamics. The Python implementation builds upon the software tools provided by the FEniCS computing platform. We describe a new tensorial approach utilizing the extension capabilities of FEniCS's Unified Form Language (UFL) to define required differential operators for tensors above second degree. The presented

更新日期：2020-07-14
• arXiv.cs.MS Pub Date : 2020-07-09
Jay P. Lim; Mridul Aanjaneya; John Gustafson; Santosh Nagarakatte

Given the importance of floating-point~(FP) performance in numerous domains, several new variants of FP and its alternatives have been proposed (e.g., Bfloat16, TensorFloat32, and Posits). These representations do not have correctly rounded math libraries. Further, the use of existing FP libraries for these new representations can produce incorrect results. This paper proposes a novel methodology for

更新日期：2020-07-13
• arXiv.cs.MS Pub Date : 2020-07-09
Deshana Desai; Etai Shuchatowitz; Zhongshi Jiang; Teseo Schneider; Daniele Panozzo

The computation of first and second-order derivatives is a staple in many computing applications, ranging from machine learning to scientific computing. We propose an algorithm to automatically differentiate algorithms written in a subset of C99 code and its efficient implementation as a Python script. We demonstrate that our algorithm enables automatic, reliable, and efficient differentiation of common

更新日期：2020-07-13
• arXiv.cs.MS Pub Date : 2020-07-09
Robert M. Corless; Erik Postma

A blend of two Taylor series for the same smooth real- or complex-valued function of a single variable can be useful for approximation. We use an explicit formula for a two-point Hermite interpolational polynomial to construct such blends. We show a robust Maple implementation that can stably and efficiently evaluate blends using linear-cost Horner form, evaluate their derivatives to arbitrary order

更新日期：2020-07-13
• arXiv.cs.MS Pub Date : 2020-07-10
Michael Herty; Jonathan Hüser; Uwe Naumann; Thomas Schilden; Wolfgang Schröder

We are interested in the development of an algorithmic differentiation framework for computing approximations to tangent vectors to scalar and systems of hyperbolic partial differential equations. The main difficulty of such a numerical method is the presence of shock waves that are resolved by proposing a numerical discretization of the calculus introduced in Bressan and Marson [Rend. Sem. Mat. Univ

更新日期：2020-07-13
• arXiv.cs.MS Pub Date : 2020-07-07
Mirko Myllykoski

The QR algorithm is one of the three phases in the process of computing the eigenvalues and the eigenvectors of a dense nonsymmetric matrix. This paper describes a task-based QR algorithm for reducing an upper Hessenberg matrix to real Schur form. The algorithm also supports generalized eigenvalue problems (QZ algorithm) but this paper focuses more on the standard case. The algorithm inherits previous

更新日期：2020-07-08
• arXiv.cs.MS Pub Date : 2020-07-03
Apostolos Chalkis; Vissarion Fisikopoulos

Sampling from high dimensional distributions and volume approximation of convex bodies are fundamental operations that appear in optimization, finance, engineering and machine learning. In this paper we present volesti, a C++ package with an R interface that provides efficient, scalable algorithms for volume estimation, uniform and Gaussian sampling from convex polytopes. volesti scales to hundreds

更新日期：2020-07-06
• arXiv.cs.MS Pub Date : 2020-07-01
Yang Liu; Pieter Ghysels; Lisa Claus; Xiaoye Sherry Li

We present a fast and approximate multifrontal solver for large-scale sparse linear systems arising from finite-difference, finite-volume or finite-element discretization of high-frequency wave equations. The proposed solver leverages the butterfly algorithm and its hierarchical matrix extension for compressing and factorizing large frontal matrices via graph-distance guided entry evaluation or randomized

更新日期：2020-07-02
• arXiv.cs.MS Pub Date : 2020-06-30
Matthias Maier; Martin Kronbichler

We discuss the efficient implementation of a high-performance second-order colocation-type finite-element scheme for solving the compressible Euler equations of gas dynamics on unstructured meshes. The solver is based on the convex limiting technique introduced by Guermond et al. (SIAM J. Sci. Comput. 40, A3211--A3239, 2018). As such it is invariant-domain preserving, i.e., the solver maintains important

更新日期：2020-07-02
• arXiv.cs.MS Pub Date : 2020-07-01
Zhenghai Chen; Tiow-Seng Tan; Hong-Yang Ong

We present a set of rules to guide the design of GPU algorithms. These rules are grounded on the principle of reducing waste in GPU utility to achieve good speed up. In accordance to these rules, we propose GPU algorithms for 2D constrained, 3D constrained and 3D Restricted Delaunay refinement problems respectively. Our algorithms take a 2D planar straight line graph (PSLG) or 3D piecewise linear complex

更新日期：2020-07-02
• arXiv.cs.MS Pub Date : 2020-06-30
Sashikumaar Ganesan; Manan Shah

Hybrid CPU-GPU algorithms for Algebraic Multigrid methods (AMG) to efficiently utilize both CPU and GPU resources are presented. In particular, hybrid AMG framework focusing on minimal utilization of GPU memory with performance on par with GPU-only implementations is developed. The hybrid AMG framework can be tuned to operate at a significantly lower GPU-memory, consequently, enables to solve large

更新日期：2020-07-02
• arXiv.cs.MS Pub Date : 2020-06-30
Hartwig Anzt; Terry Cojean; Goran Flegar; Fritz Goebel; Thomas Gruetzmacher; Pratik Nayak; Tobias Ribizel; Yu-Hsiang Tsai; Enrique S. Quintana-Orti

In this paper, we present Ginkgo, a modern C++ math library for scientific high performance computing. While classical linear algebra libraries act on matrix and vector objects, Ginkgo's design principle abstracts all functionality as "linear operators", motivating the notation of a "linear operator algebra library". Ginkgo's current focus is oriented towards providing sparse linear algebra functionality

更新日期：2020-07-01
• arXiv.cs.MS Pub Date : 2020-06-30

High fidelity scientific simulations modeling physical phenomena typically require solving large linear systems of equations which result from discretization of a partial differential equation (PDE) by some numerical method. This step often takes a vast amount of computational time to complete, and therefore presents a bottleneck in simulation work. Solving these linear systems efficiently requires

更新日期：2020-07-01
• arXiv.cs.MS Pub Date : 2020-06-30
Min Li; Yulong Ao; Chao Yang

Despite numerous efforts for optimizing the performance of Sparse Matrix and Vector Multiplication (SpMV) on modern hardware architectures, few works are done to its sparse counterpart, Sparse Matrix and Sparse Vector Multiplication (SpMSpV), not to mention dealing with input vectors of varied sparsity. The key challenge is that depending on the sparsity levels, distribution of data, and compute platform

更新日期：2020-07-01
• arXiv.cs.MS Pub Date : 2020-06-27
Xingguo Li; Tuo Zhao; Xiaoming Yuan; Han Liu

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling flexibility, estimation robustness, and tuning insensitiveness. The developed

更新日期：2020-06-30
• arXiv.cs.MS Pub Date : 2020-06-25
Yuhsiang M. TsaiKarlsruhe Institute of Technology; Terry CojeanKarlsruhe Institute of Technology; Tobias RibizelKarlsruhe Institute of Technology; Hartwig AnztKarlsruhe Institute of TechnologyUniversity of Tennessee, Innovative Computing Lab

With AMD reinforcing their ambition in the scientific high performance computing ecosystem, we extend the hardware scope of the Ginkgo linear algebra package to feature a HIP backend for AMD GPUs. In this paper, we report and discuss the porting effort from CUDA, the extension of the HIP framework to add missing features such as cooperative groups, the performance price of compiling HIP code for AMD

更新日期：2020-06-26
• arXiv.cs.MS Pub Date : 2020-06-23
Max Sagebaum; Johannes Blühdorn; Nicolas R. Gauger

For operator overloading Algorithmic Differentiation tools, the identification of primal variables and adjoint variables is usually done via indices. Two common schemes exist for their management and distribution. The linear approach is easy to implement and supports memory optimization with respect to copy statements. On the other hand, the reuse approach requires more implementation effort but results

更新日期：2020-06-24
• arXiv.cs.MS Pub Date : 2020-06-19

This work introduces a novel, fully robust and highly-scalable, $h$-adaptive aggregated unfitted finite element method for large-scale interface elliptic problems. The new method is based on a recent distributed-memory implementation of the aggregated finite element method atop a highly-scalable Cartesian forest-of-trees mesh engine. It follows the classical approach of weakly coupling nonmatching

更新日期：2020-06-22
• arXiv.cs.MS Pub Date : 2020-06-18
Charles R. Harris; K. Jarrod Millman; Stéfan J. van der Walt; Ralf Gommers; Pauli Virtanen; David Cournapeau; Eric Wieser; Julian Taylor; Sebastian Berg; Nathaniel J. Smith; Robert Kern; Matti Picus; Stephan Hoyer; Marten H. van Kerkwijk; Matthew Brett; Allan Haldane; Jaime Fernández del Río; Mark Wiebe; Pearu Peterson; Pierre Gérard-Marchant; Kevin Sheppard; Tyler Reddy; Warren Weckesser; Hameer Abbasi;

Array programming provides a powerful, compact, expressive syntax for accessing, manipulating, and operating on data in vectors, matrices, and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material science

更新日期：2020-06-19
• arXiv.cs.MS Pub Date : 2020-06-10
Denis Demidov; Lin Mu; Bin Wang

Ability to solve large sparse linear systems of equations is very important in modern numerical methods. Creating a solver with a user-friendly interface that can work in many specific scenarios is a challenging task. We describe the C ++ programming techniques that can help in creating flexible and extensible programming interfaces for linear solvers. The approach is based on policy-based design and

更新日期：2020-06-10
• arXiv.cs.MS Pub Date : 2020-06-09
Andreas Varga

In this paper we discuss the mathematical background and the computational aspects which underly the implementation of a collection of Julia functions in the MatrixPencils package for the determination of structural properties of polynomial matrices. We primarily focus on the computation of the finite and infinite spectral structures (e.g., eigenvalues, zeros, poles) as well as the left and right singular

更新日期：2020-06-09
• arXiv.cs.MS Pub Date : 2020-06-09
Santiago Badia; Alberto F. Martín; Eric Neiva; Francesc Verdugo

In this work, we present an adaptive unfitted finite element scheme that combines the aggregated finite element method with parallel adaptive mesh refinement. We introduce a novel scalable distributed-memory implementation of the resulting scheme on locally-adapted Cartesian forest-of-trees meshes. We propose a two-step algorithm to construct the finite element space at hand that carefully mixes aggregation

更新日期：2020-06-09
• arXiv.cs.MS Pub Date : 2020-06-08
Johannes Blühdorn; Nicolas R. Gauger; Matthias Kabel

We propose a universal method for the evaluation of generalized standard materials that greatly simplifies the material law implementation process. By means of automatic differentiation and a numerical integration scheme, AutoMat reduces the implementation effort to two potential functions. By moving AutoMat to the GPU, we close the performance gap to conventional evaluation routines and demonstrate

更新日期：2020-06-08
• arXiv.cs.MS Pub Date : 2020-05-27
Jian Ma

Statistical independence and conditional independence are the fundemental concepts in statistics and machine learning. Copula Entropy is a mathematical concept for multivariate statistical independence measuring and testing, and also closely related to conditional independence or transfer entropy. It has been applied to solve several statistical or machine learning problems, including association discovery

更新日期：2020-05-27
• arXiv.cs.MS Pub Date : 2020-05-22
Thomas Foster; Chon Lok Lei; Martin Robinson; David Gavaghan; Ben Lambert

High dimensional integration is essential to many areas of science, ranging from particle physics to Bayesian inference. Approximating these integrals is hard, due in part to the difficulty of locating and sampling from regions of the integration domain that make significant contributions to the overall integral. Here, we present a new algorithm called Tree Quadrature (TQ) that separates this sampling

更新日期：2020-05-22
• arXiv.cs.MS Pub Date : 2020-05-21
Randall Balestriero

SymJAX is a symbolic programming version of JAX simplifying graph input/output/updates and providing additional functionalities for general machine learning and deep learning applications. From an user perspective SymJAX provides a la Theano experience with fast graph optimization/compilation and broad hardware support, along with Lasagne-like deep learning functionalities.

更新日期：2020-05-21
• arXiv.cs.MS Pub Date : 2020-05-21
Yaniv Rubinpur; Sivan Toledo

We present robust high-performance implementations of signal-processing tasks performed by a high-throughput wildlife tracking system called ATLAS. The system tracks radio transmitters attached to wild animals by estimating the time of arrival of packets encoding known pseudo-random codes to receivers (base stations). Time-of-arrival estimation of wideband radio signals is computatoinally expensive

更新日期：2020-05-21
• arXiv.cs.MS Pub Date : 2020-05-15
Vedran Novaković

In this paper a vectorized algorithm for simultaneously computing up to eight singular value decompositions (SVDs, each of the form $A=U\Sigma V^{\ast}$) of real or complex matrices of order two is proposed. The algorithm extends to a batch of matrices of an arbitrary length $n$, that arises, for example, in the annihilation part of the parallel Kogbetliantz algorithm for the SVD of a square matrix

更新日期：2020-05-15
• arXiv.cs.MS Pub Date : 2020-05-14
Roman Iakymchuk; Maria Barreda; Stef Graillat; Jose I. Aliaga; Enrique S. Quintana-Orti

The Preconditioned Conjugate Gradient method is often employed for the solution of linear systems of equations arising in numerical simulations of physical phenomena. While being widely used, the solver is also known for its lack of accuracy while computing the residual. In this article, we propose two algorithmic solutions that originate from the ExBLAS project to enhance the accuracy of the solver

更新日期：2020-05-14
• arXiv.cs.MS Pub Date : 2020-05-13
Stefan Lenz; Maren Hackenberg; Harald Binder

Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the CRAN repository and GitHub (https://github.com/stefan-m-lenz/JuliaConnectoR), in particular for making advanced deep learning tools available. For maintainability and stability, we

更新日期：2020-05-13
• arXiv.cs.MS Pub Date : 2020-05-11
Ehsan Haghighat; Ruben Juanes

In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely used deep-learning packages Tensorflow and Keras to build deep neural networks and optimization models, thus inheriting many of Keras's functionalities, such as batch optimization and model reuse for transfer learning. SciANN is designed

更新日期：2020-05-11
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